N/A
☆☆☆☆☆
2026年5月2日
2023年3月14日
更新内容
v3.1.0• Notifications are now easier to spot with a dedicated icon on the map<br>• Map Controls are now available from the map: filter Trophies, propose new ones, and change the map style<br>• Fixed some bugs
应用信息
- 开发者
- CyberTrophy
- 分类
- Lifestyle
- 价格
- 免费
- 版本
- 3.1.0
- 安装量
- 5,000+
- 包名
- social.continuum.artravel
简介
CyberTrophy is a gamified app that encourages you to explore the real world and collect digital souvenirs called Trophies. Visit landmarks, parks, museums, events, and hidden places—then bring each Trophy to life in augmented reality when you are there. Your experience is continuous and expressive through Moments, Days, and Day Weaves. Key features: • Trophies – Discover Trophies on an interactive map and visit real-world locations to collect them. View your collection in the Showroom • Moments – Capture your experience anywhere, even without a Trophy nearby • Days – All your Trophies and Moments are organized into a unified Day. Browse your Days as a calendar or explore them on the map • Day Weave – Your movement throughout the Day becomes a continuous visual pattern, shaped by your journey and coloured by your Emotions, evolving live as you move • Friends – Connect with Friends, collect together for bonus rewards, and follow activity in the Timeline • Levels and Rewards – Earn EXP, complete Milestones, and increase your Level • Leaderboards – Compete with friends and players across cities, regions, countries, and worldwide based on Trophies collected and EXP earned CyberTrophy is designed for real-world exploration. To collect a Trophy, you must be physically nearby—it cannot be done remotely. The experience is built around going out, discovering places, and making each day meaningful. We are continuously expanding the app and welcome your feedback, ideas for new Trophies, and suggestions for improvement. We also collaborate with 3D creators and offer opportunities to showcase and reward original work. Explore, collect, capture, and shape your Day.
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基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。
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